Manipulating objects is a hallmark of human intelligence, and an important
task in domains such as robotics. In principle, Reinforcement Learning (RL)
offers a general approach to learn object manipulation. In practice, however,
domains with more than a few objects are difficult for RL agents due to the
curse of dimensionality, especially when learning from raw image observations.
In this work we propose a structured approach for visual RL that is suitable
for representing multiple objects and their interaction, and use it to learn
goal-conditioned manipulation of several objects. Key to our method is the
ability to handle goals with dependencies between the objects (e.g., moving
objects in a certain order). We further relate our architecture to the
generalization capability of the trained agent, based on a theoretical result
for compositional generalization, and demonstrate agents that learn with 3
objects but generalize to similar tasks with over 10 objects. Videos and code
are available on the project website:
this https URL

这项研究提出了一种适用于表示多个物体及其相互作用的可视强化学习结构化方法，用于学习多个物体的目标条件操纵，并演示了学习使用三个物体但能推广到具有十多个物体的类似任务的代理的能力。